• 2.1 卷积神经网络
• 2.2 卷积层
• 2.3 池化层
• 2.4 激活函数：
• 2.5 全连接层

• 4.1 Keras深度学习模型
• 4.2 Keras中重要的预定义对象
• 4.3 Keras的网络层构造

• 5.1 分为年龄、性别
• 5.2 性别分为两类
• 5.3性别训练代码
• 5.4 年龄分为七个年龄段
• 5.5 年龄训练代码

• 6.1 实现效果
• 6.2 关键代码

🔥 优质竞赛项目系列，今天要分享的是

🚩 基于深度学习的人脸年龄性别识别算法实现

该项目较为新颖，适合作为竞赛课题方向，学长非常推荐！

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：4分
• 工作量：4分
• 创新点：3分

🧿 更多资料, 项目分享：

https://gitee.com/dancheng-senior/postgraduate

1 项目课题介绍

2 关键技术

2.1 卷积神经网络

2.2 卷积层

2.3 池化层

2.4 激活函数：

2.5 全连接层

3 使用tensorflow中keras模块实现卷积神经网络

class CNN(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.conv1 = tf.keras.layers.Conv2D(
            filters=32,             # 卷积层神经元（卷积核）数目
            kernel_size=[5, 5],     # 感受野大小
            padding='same',         # padding策略（vaild 或 same）
            activation=tf.nn.relu   # 激活函数
        self.pool1 = tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2)
        self.conv2 = tf.keras.layers.Conv2D(
            filters=64,
            kernel_size=[5, 5],
            padding='same',
            activation=tf.nn.relu
        self.pool2 = tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2)
        self.flatten = tf.keras.layers.Reshape(target_shape=(7 * 7 * 64,))
        self.dense1 = tf.keras.layers.Dense(units=1024, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(units=10)
    def call(self, inputs):
        x = self.conv1(inputs)                  # [batch_size, 28, 28, 32]
        x = self.pool1(x)                       # [batch_size, 14, 14, 32]
        x = self.conv2(x)                       # [batch_size, 14, 14, 64]
        x = self.pool2(x)                       # [batch_size, 7, 7, 64]
        x = self.flatten(x)                     # [batch_size, 7 * 7 * 64]
        x = self.dense1(x)                      # [batch_size, 1024]
        x = self.dense2(x)                      # [batch_size, 10]
        output = tf.nn.softmax(x)
        return output
4 Keras介绍
 
keras是一个专门用于深度学习的开发软件。它的编程语言采用的为现在最流行的python语言，集成封装了CNTK，Tensorflow或者Theano深度学习框架为计算机后台进行深度建模，具有易于学习，高效编程的功能，数据的运算处理支持GPU和CPU，真正实现了二者的无缝切换。正是keras有如此特殊功能，所以它的优点有如下几个方面：
 
4.1 Keras深度学习模型
 
Keras深度学习模型可以分为两种：一种是序列模型，一种是通用模型。它们的区别在于其拥有不同的网络拓扑结构。序列模型是通用模型的一个范例，通常情况下应用比较广。每层之间的连接方式都是线性的，且在相邻的两
 
4.2 Keras中重要的预定义对象
 
Keras预定义了很多对象目的就是构造其网络结构，正是有了这么多的预定义对象才让Keras使用起来非常方便易用。研究中用的最多要数正则化、激活函数及初始化对象了。
 
 
正则化是在建模时防止过度拟合的最常用且效果最有效的手段之一。在神经网络中采用的手段有权重参数、偏置项以及激活函数，其分别对应的代码是kernel_regularizier、bias_regularizier以及activity_regularizier。
 
 
激活函数在网络定义中的选取十分重要。为了方便Keras预定义了丰富的激活函数，以此是适应不同的网络结构。使用激活对象的方式有两种：一个是单独定义一个激活函数层，二是通利用前置层的激活选项定义激活函数。
 
 
初始化对象是随机给定网络层激活函数kernel_initializer or bias_initializer的开始值。权重初始化值好与坏直接影响模型的训练时间的长短。
 
 
4.3 Keras的网络层构造
 
在Keras框架中，不同的网络层(Layer)定义了神经网络的具体结构。在实际网络构建中常见的用Core Layer、Convolution
 Layer、Pooling Layer、Emberdding Layer等。
 
5 数据集处理训练
 
该项目将采集的照片分为男女两个性别；‘0-9’, ‘10-19’, ‘20-29’, ‘30-39’, ‘40-49’, ‘50-59’,
 ‘60+’，七个年龄段；分别把性别和年龄段的图片分别提取出来，并保存到性别和年龄段两个文件夹下，构造如下图：
 
5.1 分为年龄、性别
 
5.2 性别分为两类
 
5.3性别训练代码
 
    # ----------------------------------------------------------------------------------------------------------------------
    # 导入一些第三方包
    # ----------------------------------------------------------------------------------------------------------------------
    import tensorflow as tf
    from nets import net
    EPOCHS = 40
    BATCH_SIZE = 32
    image_height = 128
    image_width = 128
    model_dir = "./models/age.h5"
    train_dir = "./data/age/train/"
    test_dir = "./data/age/test/"
    def get_datasets():
        train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
            rescale=1.0 / 255.0
        train_generator = train_datagen.flow_from_directory(train_dir,
                                                            target_size=(image_height, image_width),
                                                            color_mode="rgb",
                                                            batch_size=BATCH_SIZE,
                                                            shuffle=True,
                                                            class_mode="categorical")
        test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
            rescale=1.0 /255.0
        test_generator = test_datagen.flow_from_directory(test_dir,
                                                          target_size=(image_height, image_width),
                                                          color_mode="rgb",
                                                          batch_size=BATCH_SIZE,
                                                          shuffle=True,
                                                          class_mode="categorical"
        train_num = train_generator.samples
        test_num = test_generator.samples
        return train_generator, test_generator, train_num, test_num
    # ----------------------------------------------------------------------------------------------------------------------
    # 网络的初始化 ---  net.CNN(num_classes=7)
    # model.compile --- 对神经网络训练参数是设置 --- tf.keras.losses.categorical_crossentropy --- 损失函数（交叉熵）
    # tf.keras.optimizers.Adam(learning_rate=0.001) --- 优化器的选择，以及学习率的设置
    # metrics=['accuracy'] ---  List of metrics to be evaluated by the model during training and testing
    # return model --- 返回初始化之后的模型
    # ----------------------------------------------------------------------------------------------------------------------
    def get_model():
        model = net.CNN(num_classes=7)
        model.compile(loss=tf.keras.losses.categorical_crossentropy,
                      optimizer=tf.keras.optimizers.Adam(lr=0.001),
                      metrics=['accuracy'])
        return model
    if __name__ == '__main__':
        train_generator, test_generator, train_num, test_num = get_datasets()
        model = get_model()
        model.summary()
        tensorboard = tf.keras.callbacks.TensorBoard(log_dir='./log/age/')
        callback_list = [tensorboard]
        model.fit_generator(train_generator,
                            epochs=EPOCHS,
                            steps_per_epoch=train_num // BATCH_SIZE,
                            validation_data=test_generator,
                            validation_steps=test_num // BATCH_SIZE,
                            callbacks=callback_list)
        model.save(model_dir)
    # ----------------------------------------------------------------------------------------------------------------------
    # ----------------------------------------------------------------------------------------------------------------------
    # 导入一些第三方包
    # ----------------------------------------------------------------------------------------------------------------------
    import tensorflow as tf
    from nets import net
    EPOCHS = 20
    BATCH_SIZE = 32
    image_height = 128
    image_width = 128
    model_dir = "./models/gender.h5"
    train_dir = "./data/gender/train/"
    test_dir = "./data/gender/test/"
    def get_datasets():
        train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
            rescale=1.0 / 255.0
        train_generator = train_datagen.flow_from_directory(train_dir,
                                                            target_size=(image_height, image_width),
                                                            color_mode="rgb",
                                                            batch_size=BATCH_SIZE,
                                                            shuffle=True,
                                                            class_mode="categorical")
        test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
            rescale=1.0 /255.0
        test_generator = test_datagen.flow_from_directory(test_dir,
                                                          target_size=(image_height, image_width),
                                                          color_mode="rgb",
                                                          batch_size=BATCH_SIZE,
                                                          shuffle=True,
                                                          class_mode="categorical"
        train_num = train_generator.samples
        test_num = test_generator.samples
        return train_generator, test_generator, train_num, test_num
    def get_model():
        model = net.CNN(num_classes=2)
        model.compile(loss=tf.keras.losses.categorical_crossentropy,
                      optimizer=tf.keras.optimizers.Adam(lr=0.001),
                      metrics=['accuracy'])
        return model
    if __name__ == '__main__':
        train_generator, test_generator, train_num, test_num = get_datasets()
        model = get_model()
        model.summary()
        tensorboard = tf.keras.callbacks.TensorBoard(log_dir='./log/gender/')
        callback_list = [tensorboard]
        model.fit_generator(train_generator,
                            epochs=EPOCHS,
                            steps_per_epoch=train_num // BATCH_SIZE,
                            validation_data=test_generator,
                            validation_steps=test_num // BATCH_SIZE,
                            callbacks=callback_list)
        model.save(model_dir)
    # ----------------------------------------------------------------------------------------------------------------------
6.2 关键代码
 
# ----------------------------------------------------------------------------------------------------------------------
# 加载基本的库
# ----------------------------------------------------------------------------------------------------------------------
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
import os
# ----------------------------------------------------------------------------------------------------------------------
# tf.keras.models.load_model('./model/age.h5') --- 加载年龄模型
# tf.keras.models.load_model('./model/gender.h5') --- 加载性别模型
# ----------------------------------------------------------------------------------------------------------------------
model_age = tf.keras.models.load_model('./models/age.h5')
model_gender = tf.keras.models.load_model('./models/gender.h5')
# ----------------------------------------------------------------------------------------------------------------------
# 类别名称
# ----------------------------------------------------------------------------------------------------------------------
classes_age = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60+']
classes_gender = ['female', 'male']
# ----------------------------------------------------------------------------------------------------------------------
# cv2.dnn.readNetFromCaffe --- 加载人脸检测模型
# ----------------------------------------------------------------------------------------------------------------------
net = cv2.dnn.readNetFromCaffe('./models/deploy.prototxt.txt', './models/res10_300x300_ssd_iter_140000.caffemodel')
# ----------------------------------------------------------------------------------------------------------------------
# os.listdir('./images/') --- 得到文件夹列表
# ----------------------------------------------------------------------------------------------------------------------
files = os.listdir('./images/')
# ----------------------------------------------------------------------------------------------------------------------
# 遍历信息
# ----------------------------------------------------------------------------------------------------------------------
for file in files:
    # ------------------------------------------------------------------------------------------------------------------
    # image_path = './images/' + file --- 拼接得到图片文件路径
    # cv2.imread(image_path) --- 使用opencv读取图片
    # ------------------------------------------------------------------------------------------------------------------
    image_path = './images/' + file
    print(image_path)
    image = cv2.imread(image_path)
    # ------------------------------------------------------------------------------------------------------------------
    # (h, w) = image.shape[:2] --- 得到图像的高度和宽度
    # cv2.dnn.blobFromImage --- 以DNN的方式加载图像
    # net.setInput(blob) -- 设置网络的输入
    # detections = net.forward() --- 网络前相传播过程
    # ------------------------------------------------------------------------------------------------------------------
    (h, w) = image.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), 127.5)
    net.setInput(blob)
    detections = net.forward()
    # ------------------------------------------------------------------------------------------------------------------
    # for i in range(0, detections.shape[2]): --- 遍历检测结果
    # ------------------------------------------------------------------------------------------------------------------
    for i in range(0, detections.shape[2]):
        # --------------------------------------------------------------------------------------------------------------
        # confidence = detections[0, 0, i, 2] 得到检测的准确率
        # --------------------------------------------------------------------------------------------------------------
        confidence = detections[0, 0, i, 2]
        # --------------------------------------------------------------------------------------------------------------
        # if confidence > 0.85: --- 对置信度的判断
        # --------------------------------------------------------------------------------------------------------------
        if confidence > 0.85:
            # ----------------------------------------------------------------------------------------------------------
            # detections[0, 0, i, 3:7] * np.array([w, h, w, h]) --- 得到检测框的信息
            # (startX, startY, endX, endY) = box.astype("int") --- 将信息分解成左上角的x,y，以及右下角的x,y
            # cv2.rectangle --- 将人脸框起来
            # ----------------------------------------------------------------------------------------------------------
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 1)
            # ----------------------------------------------------------------------------------------------------------
            # 提取人脸部分区域
            # ----------------------------------------------------------------------------------------------------------
            roi = image[startY-15:endY+15, startX-15:endX+15]
            # ----------------------------------------------------------------------------------------------------------
            # Image.fromarray(cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)) --- 将opencv专程PIL格式的数据
            # img.resize((128, 128)) --- 改变图像的大小
            # np.array(img).reshape(-1, 128, 128, 3).astype('float32') / 255 --- 改变数据的形状，以及归一化处理
            # ----------------------------------------------------------------------------------------------------------
            img = Image.fromarray(cv2.cvtColor(roi, cv2.COLOR_BGR2RGB))
            img = img.resize((128, 128))
            img = np.array(img).reshape(-1, 128, 128, 3).astype('float32') / 255
            # ----------------------------------------------------------------------------------------------------------
            # 调用年龄识别模型得到检测结果
            # ----------------------------------------------------------------------------------------------------------
            prediction_age = model_age.predict(img)
            final_prediction_age = [result.argmax() for result in prediction_age][0]
            # ----------------------------------------------------------------------------------------------------------
            # 调用性别识别模型得到检测结果
            # ----------------------------------------------------------------------------------------------------------
            prediction_gender = model_gender.predict(img)
            final_prediction_gender = [result.argmax() for result in prediction_gender][0]
            # ----------------------------------------------------------------------------------------------------------
            # 将识别的信息拼接，然后使用cv2.putText显示
            # ----------------------------------------------------------------------------------------------------------
            res = classes_gender[final_prediction_gender] + ' ' + classes_age[final_prediction_age]
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.putText(image, str(res), (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
    # ------------------------------------------------------------------------------------------------------------------
    # ------------------------------------------------------------------------------------------------------------------
    cv2.imshow('', image)
    if cv2.waitKey(0) & 0xFF == ord('q'):
        break
🧿 更多资料, 项目分享：
 
https://gitee.com/dancheng-senior/postgraduate
论文地址：《Age and Gender Classification using Convolutional Neural Networks》
论文作者提出了一个简单的类似AlexNet的网络结构，该网络总共学习了8个年龄段：
15-20
25-32
38-43
48-53
60-100
注意：这些年龄段不是连续的
首先，要搞懂检测年龄适合用回归还...
				
Python深度学习实践：深度学习与计算机视觉的结合
作者：禅与计算机程序设计艺术 / Zen and the Art of Computer Programming
1. 背景介绍
1.1 问题的由来
# 加载人脸检测器和性别年龄分类器
face_detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
gender_age_detector = cv2.dnn.readNetFromCaffe("deploy_gender.prototxt", "gender_net.caffemodel")
# 定义性别和年龄标签
gender_labels = ["Male", "Female"]
age_labels = ["0-2", "4-6", "8-12", "15-20", "25-32", "38-43", "48-53", "60-"]
# 加载输入图像
img = cv2.imread("image.jpg")
# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 检测人脸
faces = face_detector.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(30, 30))
# 对每个检测到的人脸进行处理
for (x,y,w,h) in faces:
    # 提取人脸区域
    face_img = img[y:y+h, x:x+w].copy()
    # 将人脸区域转换为blob格式
    blob = cv2.dnn.blobFromImage(face_img, 1, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)
    # 输入blob到性别年龄分类器
    gender_age_detector.setInput(blob)
    predictions = gender_age_detector.forward()
    # 解析预测结果
    gender_prediction = gender_labels[predictions[0].argmax()]
    age_prediction = age_labels[int(math.floor(predictions[1].argmax() / 3.0))]
    # 在图像中绘制矩形和标签
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
    label = "{}, {}".format(gender_prediction, age_prediction)
    cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
# 显示结果图像
cv2.imshow("Result", img)
cv2.waitKey(0)
注意，在运行此代码之前，你需要下载并提供以下文件：
- haarcascade_frontalface_default.xml：OpenCV的人脸检测器
- deploy_gender.prototxt：性别年龄分类器的网络架构文件
- gender_net.caffemodel：性别年龄分类器的预训练权重文件